scholarly journals A Ratio Model of L1/L2 Norm for Sound Source Identification

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5290
Author(s):  
Linsen Huang ◽  
Zhongming Xu ◽  
Zhifei Zhang ◽  
Yansong He
Keyword(s):  
Sound Source ◽  
Source Identification ◽  
Regularization Parameter ◽  
Monte Carlo Analysis ◽  
Tikhonov Regularization Method ◽  
Ratio Model ◽  
Accurate Identification ◽  
Sound Source Identification ◽  
L2 Norm ◽  
Thresholding Algorithm

In the field of sound source identification, robust and accurate identification of the targeted source could be a challenging task. Most of the existing methods select the regularization parameters whose value could directly affect the accuracy of sound source identification during the solving processing. In this paper, we introduced the ratio model ℓ1/ℓ2 norm to identify the sound source(s) in the engineering field. Using the alternating direction method of multipliers solver, the proposed approach could avoid the selection of the regularization parameter and localize sound source(s) with robustness at low and medium frequencies. Compared with other three methods employing classical penalty functions, including the Tikhonov regularization method, the iterative zoom-out-thresholding algorithm and the fast iterative shrinkage-thresholding algorithm, the Monte Carlo Analysis shows that the proposed approach with ℓ1/ℓ2 model leads to stable sound pressure reconstruction results at low and medium frequencies. The proposed method demonstrates beneficial distance-adaptability and signal-to-noise ratio (SNR)-adaptability for sound source identification inverse problems.

scholarly journals Improving the Sound Source Identification Performance of Sparsity Constrained Deconvolution Beamforming Utilizing SFISTA

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Linbang Shen ◽  
Zhigang Chu ◽  
Long Tan ◽  
Debing Chen ◽  
Fangbiao Ye
Keyword(s):  
Sound Source ◽  
Source Identification ◽  
Theoretical Background ◽  
Identification Performance ◽  
Sound Source Identification ◽  
Iterative Shrinkage ◽  
Quantification Accuracy ◽  
Smoothing Parameters ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

In this paper, an alternative sparsity constrained deconvolution beamforming utilizing the smoothing fast iterative shrinkage-thresholding algorithm (SFISTA) is proposed for sound source identification. Theoretical background and solving procedures are introduced. The influence of SFISTA regularization and smoothing parameters on the sound source identification performance is analyzed, and the recommended values of the parameters are obtained for the presented cases. Compared with the sparsity constrained deconvolution approach for the mapping of acoustic sources (SC-DAMAS) and the fast iterative shrinkage-thresholding algorithm (FISTA), the proposed SFISTA with appropriate regularization and smoothing parameters has faster convergence speed, higher quantification accuracy and computational efficiency, and more insensitivity to measurement noise.

Comparisons of regularization methods and regularization parameter selection methods in sound source identification using inverse boundary element method

2019 ◽  
Vol 67 (3) ◽  
pp. 219-227
Author(s):  
Youhong Xiao ◽  
Qingqing Song ◽  
Shaowei Li ◽  
Guoxue Lv ◽  
Zhenlin Ji
Keyword(s):  
Boundary Element Method ◽  
Boundary Element ◽  
Transfer Matrix ◽  
Sound Source ◽  
Source Identification ◽  
Regularization Parameter ◽  
Vibration Velocity ◽  
Regularization Methods ◽  
Inverse Boundary ◽  
Element Method

In noise source identification based on the inverse boundary element method (IBEM), the boundary vibration velocity is predicted based on the field pressure through a transfer matrix of the vibration velocity and field pressure established on the Helmholtz integral equation. Because the matrix is often ill-posed, it needs to be regularized before reconstructing the vibration velocity. Two regularization methods and two methods of selecting the regularization parameter are investigated through the simulation analysis of a pulsating sphere. The result of transfer matrix regularization is further verified through the reconstruction of the vibration of an aluminum plate. Additionally, to reduce the large errors at some frequencies in the reconstruction result, increasing the number of measuring points is more effective than reducing the distance between the measurement plane and the sound source.

scholarly journals An investigation into high-speed train interior noise with operational transfer path analysis method

2021 ◽  
Author(s):  
Muxiao Li ◽  
Ziwei Zhu ◽  
Tiesong Deng ◽  
Xiaozhen Sheng
Keyword(s):  
Sound Source ◽  
Reference Point ◽  
Source Identification ◽  
High Speed ◽  
Interior Noise ◽  
Target Point ◽  
High Speed Train ◽  
Transfer Path ◽  
Sound Source Identification ◽  
Transfer Path Analysis

AbstractPassengers' demands for riding comfort have been getting higher and higher as the high-speed railway develops. Scientific methods to analyze the interior noise of the high-speed train are needed and the operational transfer path analysis (OTPA) method provides a theoretical basis and guidance for the noise control of the train and overcomes the shortcomings of the traditional method, which has high test efficiency and can be carried out during the working state of the targeted machine. The OTPA model is established from the aspects of "path reference point-target point" and "sound source reference point-target point". As for the mechanism of the noise transmission path, an assumption is made that the direct sound propagation is ignored, and the symmetric sound source and the symmetric path are merged. Using the operational test data and the OTPA method, combined with the results of spherical array sound source identification, the path contribution and sound source contribution of the interior noise are analyzed, respectively, from aspects of the total value and spectrum. The results show that the OTPA conforms to the calculation results of the spherical array sound source identification. At low speed, the contribution of the floor path and the contribution of the bogie sources are dominant. When the speed is greater than 300 km/h, the contribution of the roof path is dominant. Moreover, for the carriage with a pantograph, the lifted pantograph is an obvious source. The noise from the exterior sources of the train transfer into the interior mainly through the form of structural excitation, and the contribution of air excitation is non-significant. Certain analyses of train parts provide guides for the interior noise control.

Sound Source Identification Using Coherence- and Intensity-Based Methods

2007 ◽  
Vol 56 (6) ◽  
pp. 2478-2485 ◽  
Author(s):  
Giovanni Moschioni ◽  
Bortolino Saggin ◽  
Marco Tarabini
Keyword(s):  
Sound Source ◽  
Source Identification ◽  
Sound Source Identification
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